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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,220 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import fcwt\n", | ||
| "import numpy as np\n", | ||
| "import timeit" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "fs = 100\n", | ||
| "\n", | ||
| "n10k = 10000\n", | ||
| "n100k = 100000\n", | ||
| "\n", | ||
| "#Generate signals\n", | ||
| "sig_100k = np.sin(2*np.pi*((0.1+(2*np.arange(n100k))/n100k)*(np.arange(n100k)/fs)))\n", | ||
| "sig_10k = np.sin(2*np.pi*((0.1+(2*np.arange(n10k))/n10k)*(np.arange(n10k)/fs)))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "f0 = 1\n", | ||
| "f1 = 101\n", | ||
| "fn300 = 300\n", | ||
| "fn3000 = 3000\n", | ||
| "\n", | ||
| "#make sure signal is a numpy float array\n", | ||
| "sig_100k = np.array(sig_100k, dtype=np.float32)\n", | ||
| "sig_10k = np.array(sig_10k, dtype=np.float32)\n", | ||
| "\n", | ||
| "#initialize Morlet wavelet with wavelet parameter (sigma) 2.0\n", | ||
| "morl = fcwt.Morlet(2.0)\n", | ||
| "\n", | ||
| "#initialize scales\n", | ||
| "scales300 = fcwt.Scales(morl, fcwt.FCWT_LINFREQS, fs, f0, f1, fn300)\n", | ||
| "scales3000 = fcwt.Scales(morl, fcwt.FCWT_LINFREQS, fs, f0, f1, fn3000)\n", | ||
| "\n", | ||
| "#initialize fcwt\n", | ||
| "nthreads = 8\n", | ||
| "use_optimization_plan = True\n", | ||
| "use_normalization = False\n", | ||
| "fcwt_obj = fcwt.FCWT(morl, nthreads, use_optimization_plan, use_normalization)\n", | ||
| "\n", | ||
| "#initialize output array\n", | ||
| "output_10k_300 = np.zeros((fn300,sig_10k.size), dtype=np.complex64)\n", | ||
| "output_100k_300 = np.zeros((fn300,sig_100k.size), dtype=np.complex64)\n", | ||
| "output_10k_3000 = np.zeros((fn3000,sig_100k.size), dtype=np.complex64)\n", | ||
| "output_100k_3000 = np.zeros((fn3000,sig_100k.size), dtype=np.complex64)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 11, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:2048\n", | ||
| "Calculating optimal scheme for backward FFT with N:2048\n", | ||
| "Optimization schemes for N: 2048 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:4096\n", | ||
| "Calculating optimal scheme for backward FFT with N:4096\n", | ||
| "Optimization schemes for N: 4096 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:8192\n", | ||
| "Calculating optimal scheme for backward FFT with N:8192\n", | ||
| "Optimization schemes for N: 8192 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:16384\n", | ||
| "Calculating optimal scheme for backward FFT with N:16384\n", | ||
| "Optimization schemes for N: 16384 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:32768\n", | ||
| "Calculating optimal scheme for backward FFT with N:32768\n", | ||
| "Optimization schemes for N: 32768 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:65536\n", | ||
| "Calculating optimal scheme for backward FFT with N:65536\n", | ||
| "Optimization schemes for N: 65536 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n", | ||
| "Threads:8\n", | ||
| "Calculating optimal scheme for forward FFT with N:131072\n", | ||
| "Calculating optimal scheme for backward FFT with N:131072\n", | ||
| "Optimization schemes for N: 131072 have been calculated. Next time you use fCWT it will automatically choose the right optimization scheme based on number of threads and signal length.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "fcwt_obj.create_FFT_optimization_plan(100000,\"FFTW_MEASURE\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 12, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "10k-300: 0.011435500299921842 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#10k-300\n", | ||
| "a = timeit.timeit('fcwt_obj.cwt(sig_10k, scales300, output_10k_300)', number=10, globals=globals())\n", | ||
| "print(\"10k-300: \", a/10, \"seconds\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 13, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "100k-300: 0.07430814850004026 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#100k-300\n", | ||
| "a = timeit.timeit('fcwt_obj.cwt(sig_100k, scales300, output_100k_300)', number=10, globals=globals())\n", | ||
| "print(\"100k-300: \", a/10, \"seconds\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 14, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "10k-3000: 0.08949035639998329 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#10k-3000\n", | ||
| "a = timeit.timeit('fcwt_obj.cwt(sig_10k, scales3000, output_10k_3000)', number=10, globals=globals())\n", | ||
| "print(\"10k-3000: \", a/10, \"seconds\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 17, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "100k-3000: 0.6612934732000213 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#100k-3000\n", | ||
| "a = timeit.timeit('fcwt_obj.cwt(sig_100k, scales3000, output_100k_3000)', number=10, globals=globals())\n", | ||
| "print(\"100k-3000: \", a/10, \"seconds\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.9" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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